Stock Prediction Using Machine Learning Methods

Stock Prediction Using Machine Learning Methods

Er. Reshma Khan, Gagan Ajit Singh, Shubham Behal, Kartik Sharma, Saurabh Kumar

Dept. of Computer Science and Engineering,

Chandigarh  University,

Mohali, Punjab, India

Abstract— The volatility and non-linear nature of the financial stock markets make it incredibly difficult to estimate stock market returns efficiently.. Investors need rapid access to precise information while trading stocks to make intelligent selections. Programable prediction approaches have demonstrated to be increasingly successful in forecasting stock prices with the introduction of artificial intelligence and better computing capacity. However, several variables impact the decision- making process as a stock market trades multiple stocks. Furthermore, it is impossible to forecast the behavior of stock prices. All of these elements make stock price prediction, both vital and tricky. This drives research into the most accurate prediction model that creates the fewest mistakes in its projections. This research studies machine learning approaches and algorithms in an effort to increase the accuracy of stock price prediction.

Keywords— Machine Learning, Linear Regression, LSTM, SVM, Decision Tree, Random Forest